{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bvhsdk\n",
    "import ik\n",
    "import mathutils\n",
    "import skeletonmap\n",
    "import surface\n",
    "import plotanimation\n",
    "import egocentriccoord\n",
    "import numpy as np\n",
    "import os\n",
    "from pathlib import Path\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from copy import deepcopy\n",
    "from matplotlib.animation import FuncAnimation\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Abstract\n",
    "\n",
    "Motion Capture is used to record the movements of a real actor and animate virtual characters. It generates intrinsically realistic animations, since it tracks the motion of a real person. But the process to transfer the motion to a character is not straigthforward since the actor and the character may have distinct body proportions and distinct skeleton topology. The present work focus on retargeting the motion from Motion Capture to a 3D character while preserving the spatial relationship betweeen extremity joints, the hands and the feet, and the body surface. This process retain the semantic information information of movements in which the hands and the feet interact with the surface of the body, as holding the hands in front of the eyes or mouth. The Motion Retargeting process described computes new positions of the extremity joints regarding surface components of the body and adapts the pose of the virtual character in the posistions computed using Inverse Kinematics. The motion can be transfered to topologically different skeletons. The results shows that the resulting motion is being adapted accordingly to the body surface and it is as smooth as the original motion capture data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "Toy Story was released in 1995 as the first full length film produced entirely through computer animation techniques<cite data-cite=\"henne\"></cite>. Besides Pixar's production, several applications employ digital animations to entertain, convey information, educate, among others. Some exploits the use of virtual human models to make the human-computer interatction more natural and accessible. Talita, the virtual human of the TAS project<cite data-cite=\"demartino\"></cite>, is a signing avatar that communicates with deaf students using the Brazilian Sign Language. The goal of the TAS project is to help the deaf and hard of hearing to access information and in their educational process.\n",
    "\n",
    "Humans use not only the voice and facial expression cues to understand intentions, motives and wills, but also the movements of our body and limbs may carry semantic information on the emotion that one is triyng to express. The location, orientation and speed of the hand are some of the parameters that characterizes a sign language gesture, slightly discrepancies will express different messages or even make the gesture unrecognizable. Therefore, an accurate motion representation by the virtual agent should be of great concern.\n",
    "\n",
    "Keyframing is a technique to animate an avatar, the animator adjusts crucial poses of the 3D character on dispersed frames and an algorithm fills the gap between two poses by interpolating them over time, which creates the impression of the desired motion. Another digital animation technique is Motion Capture (MoCap) that aims to lessen the time-consuming and tedious work of keyframing animations. MoCap systems registers an actor performing the desired motion to animate a 3D character. Optical Motion Capture systems, for example, uses a set of cameras to track the position over time of reflective markers placed on the body surface of the performer. The motion is then transferred to the avatar, the motion retargeting process, without the need to create all the poses along the action.\n",
    "\n",
    "The motion retargeting, however, can cause ill-conditioned poses when the body proportions of the performer and the avatar are different. The motion retargeting of an actor with long arms covering his ears, as an example, to a 3D character with short arms results in a unrecognizable action, since the hands of the avatar will not reach its ears. In this case, the animator must inspect the animation, identify the irregular artifacts and correct them with keyframing, adjusting the avatar pose to the desired one. Some motion retargeting algorithms aims to avoid such artifacts to further reduce the inspection and adjustments needed by the animator.\n",
    "\n",
    "Furtheremore, the standard approach to animate 3D characters is the skeleton animation. The skeleton, a hierarchical structure of joints and bones, stores the actual motion data and the surface of the character is deformed accordingly to the rotation and translation of the skeleton. Often MoCap systems, game engines, 3D modeling and animation software built the skeleton with different specifications(cite Kitagawa), i.e., distinct topologies of the hierarchical structure. This complicates the motion retargeting process because the joints and bones of one skeleton may not exist in another or be represented in another way.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Workflow\n",
    "![Workflow](../figures/Workflow3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initial Retargeting\n",
    "\n",
    "## Skeleton Animation\n",
    "This chapter describes the Initial Motion Retargeting process, which receives the source animation and the target skeleton as inputs, both BVH files. The output is an animation of the target skeleton performing the movements of the source skeleton that can be exported as a BVH file. The success of the process does not depend on matching topologies of the skeletons, but the first pose of the source animation and the target skeleton pose must be as close as possible to the T-Pose. The output of the Initial Retargeting is the target skeleton animation. The target animation does not exploit yet the “surface-awareness” adjustments that the methods described in the subsequent sections provide, but it can already be imported in game engines or animation softwares to animate a 3D character.\n",
    "\n",
    "\n",
    "To animate a skeleton, its joints must rotate and translate along the frames, conveying the impression of motion. The hierarchical representation of the skeleton allows an all-around description of the motion by only preserving the rotational and translational values of a joint regarding its parent joint, i.e., local rotations and translations. The local transform matrix $M$ is the combination of both rotation $R$ and translation matrix $T$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\label{eq:transformmatrix}\n",
    "M = TR = \\begin{bmatrix}\n",
    "    R_{3x3} & T \\\\\n",
    "    0 & 1\n",
    "    \\end{bmatrix}\n",
    "\\end{equation}\n",
    "\n",
    "Given a joint $n$ in the skeleton, its global transform matrix $M_{global}^{n}$ is the combination of the local transform matrices of all the joints above the hierarchy. The position of the joint is computed through the global transform matrix.\n",
    "\n",
    "\\begin{equation}\n",
    "\\label{eq:transformmatrixglobal}\n",
    "M_{global}^{n} = \\prod_{i=0}^{n} M_{local}^{i}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "\\label{eq:position}\n",
    "\\mathbf{p}_{n} = M_{global}^{n}[0,0,0,1]^T\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mapping was successful\n"
     ]
    }
   ],
   "source": [
    "#Load Source Animation (Skeleton + Motion)\n",
    "#sourceAnimationPath = '..\\data\\source_animation-entender.bvh'\n",
    "sourceAnimationPath = Path(\"../data/source_animation-entender.bvh\")\n",
    "sourceAnimation = bvhsdk.ReadFile(sourceAnimationPath)\n",
    "#Load Target Skeleton\n",
    "targetSkeleton = Path(\"../data/target_skeleton.bvh\")\n",
    "targetAnimation = bvhsdk.ReadFile(targetSkeleton)\n",
    "#Load Source Surface\n",
    "sourceSurfacePath = Path(\"../data/source_surface.txt\")\n",
    "sourceSurface = surface.GetMoCapSurfaceFromTXT(sourceSurfacePath, highpolymesh = False)\n",
    "#Load Target Surface\n",
    "targetSurfacePath = Path(\"../data/target_surface.csv\")\n",
    "targetSurface = surface.GetAvatarSurfaceFromCSV(targetSurfacePath, targetAnimation, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotBVH(animation):           \n",
    "    %matplotlib qt\n",
    "    def update(frame, lines, bones):\n",
    "        for line, bone in zip(lines, range(len(lines))):\n",
    "            line.set_data([bones[frame,bone,0], bones[frame,bone,3]], [bones[frame,bone,1], bones[frame,bone,4]])\n",
    "            line.set_3d_properties([bones[frame,bone,2], bones[frame,bone,5]])\n",
    "        return lines\n",
    "\n",
    "    fig = plt.figure(figsize=(12,8))\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    #print('Preparing plot:')\n",
    "    #print('Computing Bones...')\n",
    "    #Draw Bones #######################################################\n",
    "    bones = []\n",
    "    for frame in range(animation.frames):\n",
    "        bones.append(animation.getBones(frame))\n",
    "        #if np.mod(frame+1,100) == 0:\n",
    "            #print('%i frames done.' % (int((frame+1)/100)*100))\n",
    "        \n",
    "    bones = np.asarray(bones)\n",
    "    mindata = bones.min()\n",
    "    maxdata = bones.max()\n",
    "    lines = []\n",
    "    for bone in animation.getBones(0):\n",
    "        lines.append(ax.plot([bone[0], bone[3]], [bone[1], bone[4]], [bone[2], bone[5]],'-o', color='black')[0])\n",
    "    #print('Done!')\n",
    "        \n",
    "    ax.set_xlabel('X Label')\n",
    "    ax.set_ylabel('Y Label')\n",
    "    ax.set_zlabel('Z Label')\n",
    "    ax.set_xlim(mindata,maxdata)\n",
    "    ax.set_ylim(mindata,maxdata)\n",
    "    ax.set_zlim(mindata,maxdata)\n",
    "    ani = FuncAnimation(fig, update, frames=np.arange(animation.frames), fargs=(lines, bones) ,interval=1, blit=True)\n",
    "    return ani\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot Source Animation (it may take a minute)\n",
    "#_ = PlotBVH(sourceAnimation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Skeleton Mapping\n",
    "\n",
    "Figure 2 presents two approaches to represent a virtual human skeleton. Although both can animate a humanoid-shaped model, transferring motions between the skeletons is not straightforward. A correspondence between joints of the skeletons is mandatory to identify which joints from the target skeleton should mimic the motion from the source skeleton. It is possible to infer a correspondence using the joints’ name automatically, but note that they may not be placed at the same point in the respective skeleton, as the shoulders in the figure. Therefore, the user can provide a correspondence between skeletons to properly map joints and to perform the motion retargeting (Detailed in Appendix A) (cite Monzani and Hsieh).\n",
    "\n",
    "![Skeleton Map](../figures/skelmap.png)\n",
    "\n",
    "In this work, it is required that both target and source skeletons have at least the set of joints: one hips joint, three spine joints, neck and head joints, and right and left joints for the shoulders, elbows, wrists, femurs, knees and feet. The joints referenced will be the only ones used in the Initial Retargeting proccess.\n",
    "\n",
    "## Bones Alignment\n",
    "\n",
    "Bones Alignment enforces that the vector of a mapped joint from the target skeleton pointing towards its child joint has the same direction of the correspondent vector from the source skeleton. Then, for every frame, we apply the same transform from the source skeleton joint to the correspondent target joint.\n",
    "\n",
    "The rotation matrix $R_{A}$ to align the bone vector of the target skeleton onto the bone vector of the source skeleton is calculated and applied to the global rotation of the target skeleton joint $R_{NG}^{n}$:\n",
    "\n",
    "\\begin{equation}\n",
    "        \\label{eq:newglobal}\n",
    "        R_{NG}^{n} = R_{A} R_{G}^{n}\n",
    "        \\end{equation}\n",
    "        \n",
    "Then, as expected by the BVH file format, we recover the local rotation of the joint $R_{L}^{n}$, the new global orientation is multiplied by its parent inverse rotation matrix.\n",
    "\n",
    "\\begin{equation}\n",
    "        \\label{eq:newglobal2}\n",
    "        R_{NG}^{n} = \\left(\\prod_{i=0}^{n-1} R_{L}^{i}\\right) R_{L}^{n} \n",
    "        \\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "    \\label{eq:newglobal3}\n",
    "    R_{NG}^{n} = R_{G}^{n-1} R_{L}^{n}\n",
    "    \\end{equation}\n",
    "        \n",
    "\\begin{equation}\n",
    "    \\label{eq:newglobal4}\n",
    "    R_{L}^{n} = (R_{G}^{n-1})^{-1} R_{NG}^{n}\n",
    "    \\end{equation}\n",
    "    \n",
    "In the following frames, the rotation of a joint in the source animation, from the last frame to the current one, replaces the rotation $R_{A}$ to align the correspondent joint in the target skeleton.\n",
    "\n",
    "The source motion of the root joint is decomposed into vertical and horizontal movement that are analyzed separately. The horizontal movement is the projection of the root joint position into the ground, the reason of the decomposition is the use of this projected point as a reference point for the feet (Section Surface Characterization).\n",
    "\n",
    "The ratio is given by:\n",
    "\n",
    "\\begin{equation}\n",
    "    \\label{eq:heightratio}\n",
    "    ratio = \\frac{h_{root}^{tgt}}{h_{root}^{src}}\n",
    "    \\end{equation}\n",
    "    \n",
    "where $h_{root}^{tgt}$ and $h_{root}^{src}$ are the heights of the root joint of the target and source skeletons in the first frame. The horizontal and vertical movement of the root, $g(t)$ and $h(t)$ is computed. The translation of the root joint regarding the system origin is equal to these values recombined.\n",
    "\n",
    "\\begin{equation}\n",
    "    \\label{eq:rootmov}\n",
    "    \\mathbf{g}(t)_{root}^{src} = \\mathbf{g}(t)_{root}^{tgt} ratio \\hspace{10pt} and \\hspace{10pt}\n",
    "    \\mathbf{h}(t)_{root}^{src} = \\mathbf{h}(t)_{root}^{tgt} ratio\n",
    "    \\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mapping was successful\n",
      "Starting Posture Initialization\n",
      "100 frames done. 3.5744922161102295 seconds.\n",
      "200 frames done. 2.492293119430542 seconds.\n",
      "300 frames done. 2.373246431350708 seconds.\n",
      "400 frames done. 2.4257733821868896 seconds.\n",
      "500 frames done. 2.2180330753326416 seconds.\n",
      "600 frames done. 2.406946897506714 seconds.\n"
     ]
    }
   ],
   "source": [
    "def AlignBones(tgtAnim, srcAnim, headAlign = True, spineAlign = True, handAlign = True):\n",
    "    \"\"\"\n",
    "    Align the bones of the target skeleton with the bones of the source skeleton.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    target : Animation class object\n",
    "        Input target skeleton. An Animation object with one frame. The skeleton should be in the T-Pose.\n",
    "    source : Animation class object\n",
    "        Input source animation. An Animation object that the target skeleton will copy.\n",
    "    headAlign : bool, optional\n",
    "        If this is set to True, the neck bone is aligned. This can cause strange looking pose if the skeletons have very different topologies.\n",
    "    spineAlign : bool, optional\n",
    "        If this is set to True, the spine bone is aligned. This can cause strange looking pose if the skeletons have very different topologies.\n",
    "    \"\"\"\n",
    "    #Get mapping\n",
    "    srcmap = srcAnim.getskeletonmap()\n",
    "    tgtmap = tgtAnim.getskeletonmap()\n",
    "    #\n",
    "    #Save the reference TPose\n",
    "    for joint in tgtAnim.getlistofjoints():\n",
    "        joint.tposerot = joint.rotation[0]\n",
    "        joint.tposetrans = joint.translation[0]\n",
    "    start=time.time()\n",
    "    #Expand the number of frames of the avatar animation to match the mocap animation\n",
    "    tgtAnim.expandFrames(srcmap.root.translation.shape[0])\n",
    "    #Adapt pose each frame\n",
    "    print('Starting Posture Initialization')\n",
    "    for frame in range(srcAnim.frames):\n",
    "        if np.mod(frame+1,100) == 0:\n",
    "            print('%i frames done. %s seconds.' % (int((frame+1)/100)*100,time.time()-start))\n",
    "            start=time.time()\n",
    "        #Get the Height of the root in the base position (Frame = 0)\n",
    "        #If the source animation (mocap) is not in the TPose, it will fail\n",
    "        if frame == 0:\n",
    "            ground_normal = np.array([0,1,0])\n",
    "            srcHHips = np.dot(srcmap.hips.getPosition(0), ground_normal)\n",
    "            tgtHHips = np.dot(tgtmap.hips.getPosition(0), ground_normal)\n",
    "            ratio = tgtHHips/srcHHips\n",
    "        #Adjust roots/hips height\n",
    "        #Eray Molla Eq 1\n",
    "        srcPosHips = srcmap.hips.getPosition(frame)\n",
    "        srcGroundHips = np.asarray([srcPosHips[0], 0, srcPosHips[2]])\n",
    "        tgtGroundHips = srcGroundHips*ratio\n",
    "        srcHHips = np.dot(srcmap.hips.getPosition(frame), ground_normal)\n",
    "        tgtHHips = srcHHips*ratio\n",
    "        tgtmap.root.translation[frame] = [0,tgtHHips,0] + tgtGroundHips\n",
    "        if frame == 0:\n",
    "            srcBones = []\n",
    "            tgtBones = []\n",
    "            if spineAlign:\n",
    "                srcBones.append([srcmap.hips, srcmap.spine3])\n",
    "                tgtBones.append([tgtmap.hips, tgtmap.spine3])\n",
    "            if headAlign:\n",
    "                srcBones.append([srcmap.neck, srcmap.head])\n",
    "                tgtBones.append([tgtmap.neck, tgtmap.head])\n",
    "            srcBones = srcBones + [[srcmap.rarm, srcmap.rforearm],[srcmap.larm, srcmap.lforearm],[srcmap.rforearm, srcmap.rhand],[srcmap.lforearm, srcmap.lhand],[srcmap.rupleg, srcmap.rlowleg],[srcmap.lupleg, srcmap.llowleg],[srcmap.rlowleg, srcmap.rfoot],[srcmap.llowleg, srcmap.lfoot]]\n",
    "            tgtBones = tgtBones + [[tgtmap.rarm, tgtmap.rforearm],[tgtmap.larm, tgtmap.lforearm],[tgtmap.rforearm, tgtmap.rhand],[tgtmap.lforearm, tgtmap.lhand],[tgtmap.rupleg, tgtmap.rlowleg],[tgtmap.lupleg, tgtmap.llowleg],[tgtmap.rlowleg, tgtmap.rfoot],[tgtmap.llowleg, tgtmap.lfoot]]\n",
    "            if handAlign and srcmap.lhandmiddle and srcmap.rhandmiddle and tgtmap.lhandmiddle and tgtmap.rhandmiddle:\n",
    "                srcBones = srcBones + [[srcmap.rhand, srcmap.rhandmiddle],[srcmap.lhand, srcmap.lhandmiddle]]\n",
    "                tgtBones = tgtBones + [[tgtmap.rhand, tgtmap.rhandmiddle],[tgtmap.lhand, tgtmap.lhandmiddle]]\n",
    "            for srcBone, tgtBone in zip(srcBones,tgtBones):\n",
    "                #Get source and target global transform and rotation matrices from the start of the bone\n",
    "                p0 = srcBone[0].getPosition(0)\n",
    "                p1 = srcBone[1].getPosition(0)\n",
    "                srcDirection = mathutils.unitVector(p1-p0)\n",
    "                #Get source and target global transform and rotation matrices from the end of the bone\n",
    "                p0 = tgtBone[0].getPosition(0)\n",
    "                p1 = tgtBone[1].getPosition(0)\n",
    "                tgtDirection = mathutils.unitVector(p1-p0)\n",
    "                #Align vectors\n",
    "                alignMat = mathutils.alignVectors(tgtDirection, srcDirection)\n",
    "                #Get new global rotation matrix\n",
    "                tgtGlbTransformMat = tgtBone[0].getGlobalTransform(frame)\n",
    "                tgtGlbRotationMat = mathutils.shape4ToShape3(tgtGlbTransformMat)\n",
    "                tgtNewGblRotationMat = np.dot(alignMat,tgtGlbRotationMat)\n",
    "                #Get new local rotation matrix\n",
    "                if not tgtBone[0] == tgtmap.root: #Does not have a parent, transform is already local\n",
    "                    tgtParentGblRotationMat = mathutils.shape4ToShape3(tgtBone[0].parent.getGlobalTransform(frame))\n",
    "                    tgtNewLclRotationMat = np.dot(tgtParentGblRotationMat.T, tgtNewGblRotationMat)\n",
    "                else:\n",
    "                    tgtNewLclRotationMat = tgtNewGblRotationMat[:]\n",
    "                #Get new local rotation euler angles\n",
    "                tgtNewLclRotationEuler, warning = mathutils.eulerFromMatrix(tgtNewLclRotationMat, tgtBone[0].order)\n",
    "                tgtBone[0].rotation[frame] = tgtNewLclRotationEuler\n",
    "\n",
    "        else:\n",
    "            for tgtJoint, srcJoint in zip(tgtmap.getJointsNoRootHips(), srcmap.getJointsNoRootHips()):\n",
    "                if tgtJoint is not None and srcJoint is not None:\n",
    "                    previousframe = frame-1 if frame!= 0 else 0\n",
    "                    #Get source and target global transform and rotation matrices\n",
    "                    #Even if frame == 0 the matrices need to be recalculated\n",
    "                    srcGlbTransformMat = srcJoint.getGlobalTransform(frame)\n",
    "                    srcGlbRotationMat = mathutils.shape4ToShape3(srcGlbTransformMat)\n",
    "                    tgtGlbTransformMat = tgtJoint.getGlobalTransform(previousframe)\n",
    "                    tgtGlbRotationMat = mathutils.shape4ToShape3(tgtGlbTransformMat)\n",
    "                    #Get previous source global transform and rotation matrices\n",
    "                    srcPreviousGlbTransformMat = srcJoint.getGlobalTransform(previousframe)\n",
    "                    srcPreviousGlbRotationMat = mathutils.shape4ToShape3(srcPreviousGlbTransformMat)\n",
    "                    #Get the transform of the source from the previous frame to the present frame\n",
    "                    transform = np.dot(srcGlbRotationMat, srcPreviousGlbRotationMat.T)\n",
    "                    #Apply transform\n",
    "                    tgtNewGblRotationMat = np.dot(transform, tgtGlbRotationMat)\n",
    "                    #Get new local rotation matrix\n",
    "                    tgtParentGblRotationMat = mathutils.shape4ToShape3(tgtJoint.parent.getGlobalTransform(frame))\n",
    "                    tgtNewLclRotationMat = np.dot(tgtParentGblRotationMat.T, tgtNewGblRotationMat)\n",
    "                    #Get new local rotation euler angles\n",
    "                    tgtNewLclRotationEuler, warning = mathutils.eulerFromMatrix(tgtNewLclRotationMat, tgtJoint.order)\n",
    "                    tgtJoint.rotation[frame] = tgtNewLclRotationEuler[:]\n",
    "    tgtAnim.frames = srcAnim.frames\n",
    "    tgtAnim.frametime = srcAnim.frametime\n",
    "    \n",
    "#Perform the initial retargeting\n",
    "AlignBones(targetAnimation, sourceAnimation)\n",
    "targetAnimation_aligned = deepcopy(targetAnimation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot Target Animation (this may take a few minutes)\n",
    "#_ = PlotBVH(targetAnimation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Surface Characterization\n",
    "\n",
    "## Surface Calibration\n",
    "\n",
    "Molla defined a set of points to characterize the surface of the 3D model’s and performer’s body, the yellow dots on Figure 5.1.  The points are connected throughtriangles to create a mesh that represents the body surface. The points on the surfaceof the character and the performer need to be collected as close to the relative positionas possible, since the triangle mesh differences between the characters must represent differences of the characters’ surface, thus sampling surface points from different placesintroduces noise to the analysis. The thickness of the limbs are also registered and later they are represented by capsules to avoid self-penetration in the animation.\n",
    "\n",
    "![Character Surface Calibration](../figures/TalitaPoints.png)\n",
    "\n",
    "The user obtains the surface points and radius of each limb of the character during its creation or through a 3D modeling software.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Surface Motion Estimation\n",
    "\n",
    "Collecting the position of the surface points is not enough because the performer may walk around, jump or simply twist his torso. In those cases, the sampled points do not represent the current surface position, since we do not keep track of these points. To include the surface deformation - translation and twist effects - we attach each calibration point representing the character mesh (yellow dots in Figure) to the nearest mapped joint. Thus, the point behaves as a child of the mapped joint and the transformations from every joint in the hierarchy is also applied to the point.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing Egocentric Coordinates\n",
    "We compute the egocentric coordinates of each limb joint in respect to each triangle in the surface mesh and for each capsule limb. Molla computes the projection of the joint's position $\\mathbf{p}$ on the $m$ triangles of the mesh, the projected point $\\mathbf{x}$ is called the reference point, and the displacement vector $\\mathbf{v}$ is the distance of the joint's position to the reference point. Then, the position of joint $n$ regarding the surface component $i$ is expressed as the equation: \n",
    "\n",
    "\\begin{equation}\n",
    "\\label{eq:egocoord_jointpos1}\n",
    "\\mathbf{p}_n = \\mathbf{x}_i + \\mathbf{v}_i\n",
    "\\end{equation}\n",
    "        \n",
    "The position is expressed likewise for the limbs, but the reference point is the intersection between the capsule surface and the line that passes through the center of the capsule and the joint's position. Then, combining all the surface components, limbs and meshes, the previous equation becomes:\n",
    "\n",
    "\\begin{equation}\n",
    "\\label{eq:egocoord_jointpos2}\n",
    "\\mathbf{p}_n = \\sum_{i=1}^{m}\\hat{\\lambda}_{i}(\\mathbf{x}_i + \\mathbf{v}_i)\n",
    "\\end{equation}\n",
    "\n",
    "The importance factor $\\lambda$ of a surface component is metric that encodes the proximity and orthogonality between the component and the joint's position. The goal of the importance factor is allow that surface components that are near and more perpendicular to the joint have a higher contribution on the joint's position calculation.\n",
    "\n",
    "Furthermore, a small distance between the joint and the surface may indicate that a interaction is occurring, covering the eyes with the hand as an example. In this case, we desire that the weights of the hand position that a surface component in head express excel the weights of the torso surface component, in order to preserve the interaction between the head and not with the torso. \n",
    "\n",
    "The importance factor of a surface component is the combination of the proximity $\\lambda_p$ and the orthogonality $\\lambda_{\\perp}$ properties:\n",
    "\n",
    "\\begin{equation}\n",
    "\\label{eq:importance}\n",
    "\\lambda = \\lambda_p \\lambda_{\\perp}\\hspace{0.1cm},\\hspace{0.2cm} with \\hspace{0.5cm}\\lambda_p =  \\frac{1}{||\\mathbf{v}||}\\hspace{0.5cm} and \\hspace{0.5cm}\\lambda_{\\perp} = cos(\\alpha)\n",
    "\\end{equation}\n",
    "\n",
    "    \n",
    "where $\\alpha$ is the angle between the displacement vector $\\mathbf{v}$ and the surface component normal. Finally, $\\hat{\\lambda}_{i}$ is computed as\n",
    "\n",
    "\\begin{equation}\n",
    "\\label{eq:importance_ortho}\n",
    "\\hat{\\lambda}_{i} = \\frac{\\lambda_{i}}{\\sum_{j=1}^{m}\\lambda_{j}}\n",
    "\\end{equation}\n",
    "    \n",
    "Using the target character surface information to compute the reference points and the displacement vectors from the source skeleton, and reversing Equation, we obtain the position for a target skeleton joint with the same distance from its surface as the source skeleton distance from its respective surface. But differences in the body proportions and bones length between the skeletons still were not taken into account, which will result in odd-looking poses or in a position impossible to reach.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def importanceCalc(dispvector, normal, handthick = 3.5):\n",
    "        \"\"\"\n",
    "        Computes the importance factor for the surface mesh component with normal = normal from the \n",
    "        joint with displacement vector = dispvector. We consider the hand thickness of the motion\n",
    "        capture system: you can estimate it by asking the MoCap performer to put both hands close\n",
    "        to each other (like praying), calculating the distance from each joint and dividing by 2.\n",
    "        You can also set it to zero.\n",
    "        You can change the handling of negative values equation to the one proposed by Eray Molla \n",
    "        (or your own) to compare results.\n",
    "        \"\"\"\n",
    "        epsilon = 0.01\n",
    "        #Proximity importance calculation (considering hand thickness colected from the motion capture system)\n",
    "        normdispvector = np.linalg.norm(dispvector)-handthick\n",
    "        #Handling numerical instability\n",
    "        if normdispvector <= epsilon:\n",
    "            proximity = 1/epsilon\n",
    "        else:\n",
    "            proximity = 1/normdispvector\n",
    "        normal_unit = normal/np.linalg.norm(normal)\n",
    "        dispvector_unit = dispvector/normdispvector\n",
    "        #Orthogonality importance calculation\n",
    "        #Calculating cosine\n",
    "        orthogonality = np.clip(np.dot(normal_unit, dispvector_unit), -1.0, 1.0)\n",
    "        #Handling negative values\n",
    "        orthogonality = (orthogonality+1)/2\n",
    "        #Handling numerical instability\n",
    "        if orthogonality < epsilon:\n",
    "            orthogonality = epsilon\n",
    "        orthogonality = np.abs(orthogonality)\n",
    "        return orthogonality*proximity, orthogonality, proximity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kinematic Path Normalization\n",
    "Molla proposes the Kinematic Path Normalization to adjust the joints' position according to the length of the bones in the skeleton. The kinematic path is the route through joints and bone segments from a reference point to a limb joint. Figure BLA represents the kinematic path for the right hand to the root.\n",
    "\n",
    "Note that the goal of the Kinematic Path Normalization is to adjust the position of the joint accordingly to the length of the bone segments. Thus, since the source and target skeleton may have different topologies, we consider a set of bone segments that can be mapped between skeleton with distinct levels of details. This set of bones represent a basic skeleton with the bones: spine, head, and right and left clavicle, arm, forearm, femur, thight and shin.\n",
    "\n",
    "Given the path along the skeleton from the joint being evaluated until the nearest joint of the surface component or until the extremity joint for limbs capsules, we compute the displacement vector as\n",
    "\n",
    "\\begin{equation}\n",
    "\\label{eq:dispvector}\n",
    "\\mathbf{v} = \\mathbf{-x}_{j0} + \\sum_{i=1}^{n} \\mathbf{s}_i\n",
    "\\end{equation}\n",
    "\n",
    "<img src=\"../figures/SkeletonKinematicPath.svg\" alt=\"drawing\" width=\"300\"/>\n",
    "\n",
    "\n",
    "Molla computes the contribution of each bone segment $\\mathbf{s}_i$ in the kinematic path to the position of the joint relative to the surface, that is, the displacement vector $\\mathbf{v}$, based on the projection of each bone vector onto the displacement vector by\n",
    "\n",
    "\\begin{equation}\n",
    "\\label{eq:contribution}\n",
    "cos(\\alpha_{i}) = \\frac{\\mathbf{v}}{||\\mathbf{v}||}\\cdot\\frac{\\mathbf{s}_{i}}{||\\mathbf{s}_{i}||}\n",
    "\\end{equation}\n",
    "\n",
    "The cosines of every bone in the kinematic path are kept as $C_{i} = \\{|cos(\\alpha_{1})|, |cos(\\alpha_{2})|,..., |cos(\\alpha_{n})|\\}$ for a kinematic path with $n$ bone segments. To adapt the joint position in the target skeleton, the displacement vector is normalized as\n",
    "\n",
    "\\begin{equation}\n",
    "\\label{eq:tau}\n",
    "\\mathbf{\\hat{v}} = \\frac{\\mathbf{v}}{\\tau},\\ where\\ \\tau = \\sum_{i=0}^{n}||\\mathbf{s}_{i}||\\ |cos(\\alpha_{i})|\n",
    "\\end{equation}\n",
    "\n",
    "## Summary\n",
    "Given a joint $j$ and a surface component $i$, we store the set of parametes: $\\mathbf{e}_{j,i} = (\\mathbf{\\hat{\\lambda}}_{i}$, $\\mathbf{\\hat{v}}$, $C_{i})$. The egocentric coordinates of a joint $E_{j}$ for a given frame considering all $m$ surface components is given by\n",
    "\n",
    "\\begin{equation}\n",
    "\\label{eq:egocoordsall}\n",
    "E_{j} = \\{e_{j,1}, e_{j,2},...,e_{j,m}\\}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pathnormCalc(joint, animation, mesh, frame, refpoint, vectors, surface = None):\n",
    "    \"\"\"\n",
    "    Computes the kinematic path normalization for each limb joint (hands, elbows, knees and feet).\n",
    "    The foot contact with the ground will be included in future versions.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    joint : \n",
    "    animation : \n",
    "    mesh : \n",
    "    frame : \n",
    "    refpoint : \n",
    "    vectors : \n",
    "    surface : \n",
    "    \"\"\"\n",
    "    #Get bone vectors\n",
    "    lvec_fore, rvec_fore, lvec_arm, rvec_arm, lvec_clavicle, rvec_clavicle, vec_neck, vec_spine, lvec_femur, rvec_femur, lvec_upleg, rvec_upleg, lvec_lowleg, rvec_lowleg = vectors\n",
    "    #Get mapped joints\n",
    "    lhand, rhand, lforearm, rforearm = animation.getskeletonmap().lhand, animation.getskeletonmap().rhand, animation.getskeletonmap().lforearm, animation.getskeletonmap().rforearm\n",
    "    lfoot, rfoot, llowleg, rlowleg = animation.getskeletonmap().lfoot, animation.getskeletonmap().rfoot, animation.getskeletonmap().llowleg, animation.getskeletonmap().rlowleg\n",
    "    #Defines the kinematic path for each joint\n",
    "    if joint == lhand:\n",
    "        kinpath = np.asarray([lvec_clavicle, lvec_arm, lvec_fore])\n",
    "    elif joint == rhand:\n",
    "        kinpath = np.asarray([rvec_clavicle, rvec_arm, rvec_fore])\n",
    "    elif joint == lforearm:\n",
    "        kinpath = np.asarray([lvec_clavicle, lvec_arm])\n",
    "    elif joint == rforearm:\n",
    "        kinpath = np.asarray([rvec_clavicle, rvec_arm])\n",
    "    elif joint == lfoot:\n",
    "        kinpath = np.asarray([lvec_femur, lvec_upleg, lvec_lowleg])\n",
    "    elif joint == rfoot:\n",
    "        kinpath = np.asarray([rvec_femur, rvec_upleg, rvec_lowleg])\n",
    "    elif joint == llowleg:\n",
    "        kinpath = np.asarray([lvec_femur, lvec_upleg])\n",
    "    elif joint == rlowleg:\n",
    "        kinpath = np.asarray([rvec_femur, rvec_upleg])\n",
    "\n",
    "    #Get vector displacement\n",
    "    if joint == lhand or joint == rhand or joint == lforearm or joint == rforearm:\n",
    "        cos = np.empty(len(kinpath)+1)\n",
    "        #Upper limb\n",
    "        if mesh == 'head':\n",
    "            vec_displacement = -(refpoint - animation.getskeletonmap().head.getPosition(frame)) + vec_neck\n",
    "            vec_displacement = vec_displacement + kinpath.sum(axis = 0)\n",
    "            cos[0] = mathutils.cosBetween(vec_displacement, vec_neck)\n",
    "            tau = np.linalg.norm(vec_neck)*cos[0]\n",
    "        elif mesh == 'body':\n",
    "            vec_displacement = -(refpoint - animation.getskeletonmap().hips.getPosition(frame)) + vec_spine\n",
    "            vec_displacement = vec_displacement + kinpath.sum(axis = 0)\n",
    "            cos[0] = mathutils.cosBetween(vec_displacement, vec_spine)\n",
    "            tau = np.linalg.norm(vec_spine)*cos[0]\n",
    "        else:\n",
    "            raise Exception('Upper limb joints only accept meshes from the head and body.')\n",
    "        #Get tau (Eray Molla Eq 5)\n",
    "        for i in range(1,len(cos)):\n",
    "            cos[i] = mathutils.cosBetween(vec_displacement, kinpath[i-1])\n",
    "            tau = tau + np.linalg.norm(kinpath[i-1])*cos[i]\n",
    "    else:\n",
    "        #Lower limbs\n",
    "        if mesh == 'head':\n",
    "            cos = np.empty(len(kinpath)+2)\n",
    "            vec_displacement = -(refpoint - animation.getskeletonmap().head.getPosition(frame)) + vec_neck - vec_spine\n",
    "            vec_displacement = vec_displacement + kinpath.sum(axis = 0)\n",
    "            cos[0] = mathutils.cosBetween(vec_displacement, vec_neck)\n",
    "            cos[1] = mathutils.cosBetween(vec_displacement, -vec_spine)\n",
    "            tau = np.linalg.norm(vec_neck)*cos[0] + np.linalg.norm(-vec_spine)*cos[1]\n",
    "            for i in range(2,len(cos)):\n",
    "                cos[i] = mathutils.cosBetween(vec_displacement, kinpath[i-2])\n",
    "                tau = tau + np.linalg.norm(kinpath[i-2])*cos[i]\n",
    "        elif mesh == 'body':\n",
    "            cos = np.empty(len(kinpath))\n",
    "            vec_displacement = -(refpoint - animation.getskeletonmap().hips.getPosition(frame))\n",
    "            vec_displacement = vec_displacement + kinpath.sum(axis = 0)\n",
    "            tau = 0\n",
    "            for i in range(len(cos)):\n",
    "                cos[i] = mathutils.cosBetween(vec_displacement, kinpath[i])\n",
    "                tau = tau + np.linalg.norm(kinpath[i])*cos[i]\n",
    "        elif mesh == 'ground':\n",
    "            assert joint == rfoot or joint == lfoot, 'Foot contact should only be randled with the right and left foot'\n",
    "            hipsPosition = animation.getskeletonmap().hips.getPosition(frame)\n",
    "            hipsGround = np.asarray([hipsPosition[0], 0, hipsPosition[2]])\n",
    "            hipsHeight = np.asarray([0, hipsPosition[1], 0])\n",
    "            vec_displacement = -(refpoint - hipsGround) + hipsHeight\n",
    "            vec_displacement = vec_displacement+ kinpath.sum(axis = 0)\n",
    "            cos = np.empty(len(kinpath)+1)\n",
    "            cos[0] = mathutils.cosBetween(vec_displacement, hipsHeight)\n",
    "            tau = 0\n",
    "            for i in range(1,len(cos)):\n",
    "                cos[i] = mathutils.cosBetween(vec_displacement, kinpath[i-1])\n",
    "                tau = tau + np.linalg.norm(kinpath[i-1])*cos[i]\n",
    "    return vec_displacement/tau, cos, tau\n",
    "\n",
    "def GetEgocentricCoordinatesTargets(mocap, surfaceMocap, avatar, surfaceAvatar):\n",
    "    \"\"\"\n",
    "    Get the egocentric coordinates and returns the new positions for each limb joint.\n",
    "    By default we only compute for the left and right hand joints, you can remove the comments\n",
    "    in the lines \"egocentriccoord.EgocentricCoordinate(joint)\" to compute the coordinates and\n",
    "    targets for the joints you wish, bu it will severely increase the execution time.\n",
    "    \"\"\"\n",
    "    headmesh = surfaceMocap.headmesh\n",
    "    bodymesh = surfaceMocap.bodymesh\n",
    "    headmesh_avatar = surfaceAvatar.headmesh\n",
    "    bodymesh_avatar = surfaceAvatar.bodymesh\n",
    "    ego = None\n",
    "    egocentriccoord.EgocentricCoordinate.clean()\n",
    "\n",
    "    #Get source skeleton map\n",
    "    mocap_skmap = mocap.getskeletonmap()\n",
    "    lhand, rhand = mocap_skmap.lhand, mocap_skmap.rhand\n",
    "    lforearm, rforearm = mocap_skmap.lforearm, mocap_skmap.rforearm\n",
    "    larm, rarm = mocap_skmap.larm, mocap_skmap.rarm\n",
    "    lupleg, rupleg = mocap_skmap.lupleg, mocap_skmap.rupleg\n",
    "    llowleg, rlowleg = mocap_skmap.llowleg, mocap_skmap.rlowleg\n",
    "    lfoot, rfoot = mocap_skmap.lfoot, mocap_skmap.rfoot\n",
    "\n",
    "    #Get target skeleton map\n",
    "    ava_skmap = avatar.getskeletonmap()\n",
    "    lhand_ava, rhand_ava = ava_skmap.lhand, ava_skmap.rhand\n",
    "    lforearm_ava, rforearm_ava = ava_skmap.lforearm, ava_skmap.rforearm\n",
    "    larm_ava, rarm_ava = ava_skmap.larm, ava_skmap.rarm\n",
    "    lupleg_ava, rupleg_ava = ava_skmap.lupleg, ava_skmap.rupleg\n",
    "    llowleg_ava, rlowleg_ava = ava_skmap.llowleg, ava_skmap.rlowleg\n",
    "    lfoot_ava, rfoot_ava = ava_skmap.lfoot, ava_skmap.rfoot\n",
    "\n",
    "    start=time.time()\n",
    "\n",
    "    ground_normal = np.asarray([0,1,0])\n",
    "\n",
    "    egocentriccoord.EgocentricCoordinate(rhand)\n",
    "    egocentriccoord.EgocentricCoordinate(lhand)\n",
    "    egocentriccoord.EgocentricCoordinate(rforearm)\n",
    "    egocentriccoord.EgocentricCoordinate(lforearm)\n",
    "    egocentriccoord.EgocentricCoordinate(rfoot)\n",
    "    egocentriccoord.EgocentricCoordinate(lfoot)\n",
    "    egocentriccoord.EgocentricCoordinate(rlowleg)\n",
    "    egocentriccoord.EgocentricCoordinate(llowleg)\n",
    "\n",
    "    #Para cada frame\n",
    "    for frame in range(mocap.frames):\n",
    "        if np.mod(frame+1,100) == 0:\n",
    "            print('%i frames done. %s seconds.' % (int((frame+1)/100)*100,time.time()-start))\n",
    "            start=time.time()\n",
    "\n",
    "        vectors = egocentriccoord.getVectors(mocap, frame)\n",
    "        jointpositions = egocentriccoord.getJointsPositions(mocap, frame)     \n",
    "        mesh = egocentriccoord.getMeshPositions(mocap, surfaceMocap, frame)\n",
    "        \n",
    "        #Para cada junta\n",
    "        #for joint in [rhand, lhand, rforearm, lforearm, rfoot, lfoot, rlowleg, llowleg]:\n",
    "        for joint in [rhand, lhand]:\n",
    "            ego = egocentriccoord.EgocentricCoordinate.getCoord(joint.name).addCoordFrame(frame)\n",
    "            jointPosition = joint.getPosition(frame)\n",
    "\n",
    "            #Eray Molla Equation 3\n",
    "            #Get the surface normal of extrimities joints\n",
    "            if joint == rhand or joint == lhand or joint == rfoot or joint == lfoot:\n",
    "                jointSurfaceNormal = egocentriccoord.extremityNormal(mocap, joint, frame)\n",
    "\n",
    "            #Mesh components\n",
    "            for i in range(len(bodymesh)+len(headmesh)):\n",
    "                if i<len(headmesh):\n",
    "                    refpoint, dispvector, normal = mathutils.distFromCentroid(jointPosition, mesh[i][0], mesh[i][1], mesh[i][2])\n",
    "                    dispvector_norm, normcoef, tau = pathnormCalc(joint, mocap, 'head', frame, refpoint, vectors, jointpositions)\n",
    "                else:\n",
    "                    j = i-len(headmesh)\n",
    "                    refpoint, dispvector, normal = mathutils.distFromCentroid(jointPosition, mesh[i][0], mesh[i][1], mesh[i][2])\n",
    "                    dispvector_norm, normcoef, tau = pathnormCalc(joint, mocap, 'body', frame, refpoint, vectors, jointpositions)\n",
    "\n",
    "                importance, ortho, proxi = importanceCalc(dispvector, normal)\n",
    "                #Importance\n",
    "                ego.ortho.append(ortho)\n",
    "                ego.proxi.append(proxi)\n",
    "                ego.importance.append(importance)\n",
    "                #Reference point (triangle mesh)\n",
    "                ego.refpoint.append(refpoint)\n",
    "                #Displacement Vector (distance from refpoint to the joint position)\n",
    "                ego.dispvector.append(dispvector_norm)\n",
    "                #Cosines between each bone and the displacement vector Eray Molla Eq 4\n",
    "                ego.normcoef.append(normcoef)\n",
    "                #Normalization factor Eray Molla Eq 5\n",
    "                ego.tau.append(tau)\n",
    "                ego.normal.append(normal)\n",
    "\n",
    "                #Eray Molla Equation 3\n",
    "                if joint == rhand or joint == lhand or joint == rfoot or joint == lfoot:\n",
    "                    angle,_ = mathutils.angleBetween(normal, jointSurfaceNormal)\n",
    "                    ego.angle.append(angle)\n",
    "\n",
    "            #Limbs components\n",
    "            for values_returned in egocentriccoord.pathnormCalcLimb(joint, mocap, 'limb', frame, vectors, jointpositions, surfaceMocap):\n",
    "                dispvector, normcoef, tau, limbname, normal, refpoint, refpoint_aux = values_returned\n",
    "                importance, ortho, proxi = egocentriccoord.importanceCalcLimb(vectors, limbname, dispvector, normal)\n",
    "                ego.ortho.append(ortho)\n",
    "                ego.proxi.append(proxi)\n",
    "                ego.importance.append(importance)\n",
    "                ego.refpoint.append(refpoint)\n",
    "                ego.dispvector.append(dispvector/tau)\n",
    "                ego.normcoef.append(normcoef)\n",
    "                ego.tau.append(tau)\n",
    "                ego.normal.append(normal)\n",
    "                #Eray Molla Equation 3\n",
    "                if joint == rhand or joint == lhand or joint == rfoot or joint == lfoot:\n",
    "                    angle,_ = mathutils.angleBetween(normal, jointSurfaceNormal)\n",
    "                    ego.angle.append(angle)\n",
    "\n",
    "            #Add the ground projection as a reference point\n",
    "            if joint == rfoot or joint == lfoot:\n",
    "                refpoint = np.asarray([jointPosition[0], 0,jointPosition[2]])\n",
    "                dispvector_norm, normcoef, tau = pathnormCalc(joint, mocap, 'ground', frame, refpoint, vectors, jointpositions)\n",
    "                importance, ortho, proxi = importanceCalc(dispvector, ground_normal)\n",
    "                ego.ortho.append(ortho)\n",
    "                ego.proxi.append(proxi)\n",
    "                ego.importance.append(importance)\n",
    "                ego.refpoint.append(refpoint)\n",
    "                ego.dispvector.append(dispvector_norm)\n",
    "                ego.normcoef.append(normcoef)\n",
    "                ego.tau.append(tau)\n",
    "                ego.normal.append(normal)\n",
    "                angle,_ = mathutils.angleBetween(ground_normal, jointSurfaceNormal)\n",
    "                ego.angle.append(angle)\n",
    "\n",
    "            #distance between point p0=jointPosition and line passing through p1 and p2:\n",
    "            # d = |(p0 - p1) x (p0 - p2)|/|p2-p1|\n",
    "#            distance = np.linalg.norm(np.cross(jointPosition - p1,jointPosition - p2))/np.linalg.norm(p2 - p1)\n",
    "#            dispvector = distance - surfaceMocap.getPoint('foreRight').radius\n",
    "\n",
    "            #Normaliza a importancia\n",
    "            sumimp = sum(ego.importance)\n",
    "            ego.importance = np.asarray([ego.importance[element]/sumimp for element in range(len(ego.importance))])\n",
    "\n",
    "\n",
    "        #####################################################################################\n",
    "        # Desnormalizando a cada frame\n",
    "        #####################################################################################\n",
    "\n",
    "        \n",
    "        vectors = egocentriccoord.getVectors(avatar, frame)\n",
    "        jointpositions = egocentriccoord.getJointsPositions(avatar, frame)     \n",
    "        mesh = egocentriccoord.getMeshPositions(avatar, surfaceAvatar, frame)\n",
    "        lvec_fore, rvec_fore, lvec_arm, rvec_arm, lvec_clavicle, rvec_clavicle, vec_neck, vec_spine, lvec_femur, rvec_femur, lvec_upleg, rvec_upleg, lvec_lowleg, rvec_lowleg = vectors\n",
    "\n",
    "\n",
    "        #For each EE (each hand)\n",
    "        #for joint,egoindex in zip([rhand_ava, lhand_ava, rforearm_ava, lforearm_ava, rfoot_ava, lfoot_ava, rlowleg_ava, llowleg_ava],range(6)):\n",
    "        for joint,egoindex in zip([rhand_ava, lhand_ava],range(2)):\n",
    "            #Get the ego coordinates of the mocap animation joint\n",
    "            # aux_jointname = skeletonmap.getmatchingjoint(joint.name, mocap).name\n",
    "            ego = egocentriccoord.EgocentricCoordinate.egolist[egoindex].getCoordFrame(frame)\n",
    "\n",
    "            #For each mesh triangle\n",
    "            vec_displacement = []\n",
    "            de_refpoint = []\n",
    "            position = []\n",
    "            taulist = []\n",
    "            normallist = []\n",
    "            for i in range(len(bodymesh_avatar)+len(headmesh_avatar)):\n",
    "                if i<len(headmesh_avatar):\n",
    "                    de_refpoint_aux, normal = mathutils.getCentroid(mesh[i][0], mesh[i][1], mesh[i][2])\n",
    "                    if joint == lhand_ava: kinpath = np.asarray([vec_neck, lvec_clavicle, lvec_arm, lvec_fore])\n",
    "                    elif joint == rhand_ava: kinpath = np.asarray([vec_neck, rvec_clavicle, rvec_arm, rvec_fore])\n",
    "                    elif joint == lforearm_ava: kinpath = np.asarray([vec_neck, lvec_clavicle, lvec_arm])\n",
    "                    elif joint == rforearm_ava: kinpath = np.asarray([vec_neck, rvec_clavicle, rvec_arm])\n",
    "                    elif joint == lfoot_ava: kinpath = np.asarray([vec_neck, vec_spine, lvec_femur, lvec_upleg, lvec_lowleg])\n",
    "                    elif joint == rfoot_ava: kinpath = np.asarray([vec_neck, vec_spine, rvec_femur, rvec_upleg, rvec_lowleg])\n",
    "                    elif joint == llowleg_ava: kinpath = np.asarray([vec_neck, vec_spine, lvec_femur, lvec_upleg])\n",
    "                    elif joint == rlowleg_ava: kinpath = np.asarray([vec_neck, vec_spine, rvec_femur, rvec_upleg])\n",
    "                else:\n",
    "                    j = i-len(headmesh_avatar)\n",
    "                    de_refpoint_aux, normal = mathutils.getCentroid(mesh[i][0], mesh[i][1], mesh[i][2])\n",
    "                    if joint == lhand_ava: kinpath = np.asarray([vec_spine, lvec_clavicle, lvec_arm, lvec_fore])\n",
    "                    elif joint == rhand_ava: kinpath = np.asarray([vec_spine, rvec_clavicle, rvec_arm, rvec_fore])\n",
    "                    elif joint == lforearm_ava: kinpath = np.asarray([vec_spine, lvec_clavicle, lvec_arm])\n",
    "                    elif joint == rforearm_ava: kinpath = np.asarray([vec_spine, rvec_clavicle, rvec_arm])\n",
    "                    elif joint == lfoot_ava: kinpath = np.asarray([lvec_femur, lvec_upleg, lvec_lowleg])\n",
    "                    elif joint == rfoot_ava: kinpath = np.asarray([rvec_femur, rvec_upleg, rvec_lowleg])\n",
    "                    elif joint == llowleg_ava: kinpath = np.asarray([lvec_femur, lvec_upleg])\n",
    "                    elif joint == rlowleg_ava: kinpath = np.asarray([rvec_femur, rvec_upleg])\n",
    "                tau = (np.linalg.norm(kinpath, axis = 1)*ego.normcoef[i]).sum()\n",
    "                vec_displacement_aux = ego.dispvector[i]*tau\n",
    "                taulist.append(tau)\n",
    "                vec_displacement.append(vec_displacement_aux)\n",
    "                de_refpoint.append(de_refpoint_aux)\n",
    "                position.append(vec_displacement_aux+de_refpoint_aux)\n",
    "                normallist.append(normal)\n",
    "\n",
    "            #Get limb coordinates\n",
    "            for values_returned in egocentriccoord.DenormEgoLimb(joint, avatar, surfaceAvatar, frame, vectors, jointpositions, ego, i+1):\n",
    "                vec_displacement_aux, de_refpoint_aux, tau, normal = values_returned\n",
    "                taulist.append(tau)\n",
    "                vec_displacement.append(vec_displacement_aux)\n",
    "                de_refpoint.append(de_refpoint_aux)\n",
    "                position.append(vec_displacement_aux+de_refpoint_aux)\n",
    "                normallist.append(normal)\n",
    "\n",
    "\n",
    "            if joint == rfoot_ava or joint == lfoot_ava:\n",
    "                jointPosition = joint.getPosition(frame)\n",
    "                hipsPosition = avatar.getskeletonmap().hips.getPosition(frame)\n",
    "                hipsGround = np.asarray([hipsPosition[0], 0, hipsPosition[2]])\n",
    "                hipsHeight = np.asarray([0, hipsPosition[1], 0])\n",
    "                de_refpoint_aux = np.asarray([jointPosition[0], 0, jointPosition[2]])\n",
    "                if joint == rfoot:\n",
    "                    kinpath = np.asarray([-de_refpoint_aux, -hipsGround, hipsHeight, rvec_femur, rvec_upleg, rvec_lowleg])\n",
    "                else:\n",
    "                    kinpath = np.asarray([-de_refpoint_aux, -hipsGround, hipsHeight, lvec_femur, lvec_upleg, lvec_lowleg])\n",
    "                vec_displacement_aux = kinpath.sum(axis = 0)\n",
    "                cos = np.empty(len(kinpath))\n",
    "                tau = 0\n",
    "                for i in range(len(cos)):\n",
    "                    cos[i] = mathutils.cosBetween(vec_displacement_aux, kinpath[i])\n",
    "                    tau = tau + np.linalg.norm(kinpath[i])*cos[i]\n",
    "                vec_displacement_aux = ego.dispvector[-1]*tau\n",
    "                taulist.append(tau)\n",
    "                vec_displacement.append(vec_displacement_aux)\n",
    "                de_refpoint.append(de_refpoint_aux)\n",
    "                position.append(vec_displacement_aux+de_refpoint_aux)\n",
    "                normallist.append([0,1,0])\n",
    "\n",
    "\n",
    "            ego.tgt_dispvector = np.asarray(vec_displacement)\n",
    "            ego.tgt_tau = np.asarray(taulist)\n",
    "            ego.tgt_refpoint = np.asarray(de_refpoint)\n",
    "            ego.targets = np.asarray(position)\n",
    "            ego.tgt_normal = np.asarray(normallist)\n",
    "    return egocentriccoord.EgocentricCoordinate.egolist#targets, taulist, vec_displacement\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 frames done. 11.339604377746582 seconds.\n",
      "200 frames done. 9.986479043960571 seconds.\n",
      "300 frames done. 9.843039274215698 seconds.\n",
      "400 frames done. 9.849372863769531 seconds.\n",
      "500 frames done. 9.810274839401245 seconds.\n",
      "600 frames done. 9.979029178619385 seconds.\n"
     ]
    }
   ],
   "source": [
    "#This will take a few minutes\n",
    "egoCoords = GetEgocentricCoordinatesTargets(sourceAnimation, sourceSurface, targetAnimation, targetSurface)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def PlotDispvectorsFrames(srcAnim, srcSurface, tgtAnim, tgtSurface, egoCoord, f1, f2, f3):\n",
    "    %matplotlib inline\n",
    "    fig, axs = plt.subplots(2, 3, figsize=(16,8))\n",
    "    \n",
    "    min_X, max_X = np.inf,np.NINF\n",
    "    min_Y, max_Y = np.inf,np.NINF\n",
    "    for animation, surface, line in zip([srcAnim, tgtAnim],[srcSurface, tgtSurface],range(2)):\n",
    "        for frame, column in zip([f1,f2,f3],range(3)):\n",
    "            aux = animation.getBones(frame)\n",
    "            bones = []\n",
    "            for i in range(len(aux)):\n",
    "                axs[line, column].plot([aux[i,0], aux[i,3]], [aux[i,1], aux[i,4]],'-o', color='black')\n",
    "            surf = []\n",
    "            for triangle in surface.headmesh:\n",
    "                vertices = [[vert.getPosition(animation,frame)[0],vert.getPosition(animation,frame)[1]] for vert in triangle]\n",
    "                vertices.append([triangle[0].getPosition(animation,frame)[0],triangle[0].getPosition(animation,frame)[1]])\n",
    "                vertices = np.asarray(vertices)\n",
    "                axs[line, column].plot(vertices[:,0],vertices[:,1],'-o', color='red', markersize=1, alpha = 0.5)\n",
    "\n",
    "            for triangle in surface.bodymesh:\n",
    "                vertices = [[vert.getPosition(animation,frame)[0],vert.getPosition(animation,frame)[1]] for vert in triangle]\n",
    "                vertices.append([triangle[0].getPosition(animation,frame)[0],triangle[0].getPosition(animation,frame)[1]])\n",
    "                vertices = np.asarray(vertices)\n",
    "                axs[line, column].plot(vertices[:,0],vertices[:,1],'-o', color='red', markersize=1, alpha = 0.5)\n",
    "            mini_X,maxi_X = np.min([aux[:,0].min(),aux[:,3].min()]), np.max([aux[:,0].max(),aux[:,3].max()])\n",
    "            mini_Y, maxi_Y = np.min([aux[:,1].min(),aux[:,4].min()]), np.max([aux[:,1].max(),aux[:,4].max()])\n",
    "            if mini_X<min_X: \n",
    "                min_X=mini_X\n",
    "            if maxi_X>max_X: \n",
    "                max_X=maxi_X\n",
    "            if mini_Y<min_Y: \n",
    "                min_Y=mini_Y\n",
    "            if maxi_Y>max_Y: \n",
    "                max_Y=maxi_Y\n",
    "                \n",
    "    lenNoLimb = len(srcSurface.headmesh)+len(srcSurface.bodymesh)\n",
    "    for frame, column in zip([f1,f2,f3],range(3)):        \n",
    "        dispvector = np.asarray([np.asarray(egoCoord.framecoord[frame].dispvector[i]*egoCoord.framecoord[frame].tau[i])+egoCoord.framecoord[frame].refpoint[i] for i in range(lenNoLimb)])\n",
    "        refpoint = np.asarray(egoCoord.framecoord[frame].refpoint[:lenNoLimb])\n",
    "        norm_importance = egoCoord.framecoord[frame].importance/egoCoord.framecoord[frame].importance.max()\n",
    "        for i in range(lenNoLimb):\n",
    "            axs[0, column].plot([refpoint[i,0],dispvector[i,0]],[refpoint[i,1],dispvector[i,1]], color=[np.clip(1*(1-norm_importance[i]),0,0.8),np.clip(1*(1-norm_importance[i]),0,0.8),1,0.8])\n",
    "        dispvector = np.asarray([np.asarray(egoCoord.framecoord[frame].tgt_dispvector[i])+egoCoord.framecoord[frame].tgt_refpoint[i] for i in range(lenNoLimb)])\n",
    "        refpoint = np.asarray(egoCoord.framecoord[frame].tgt_refpoint[:lenNoLimb])\n",
    "        norm_importance = egoCoord.framecoord[frame].importance/egoCoord.framecoord[frame].importance.max()\n",
    "        for i in range(lenNoLimb):\n",
    "            axs[1, column].plot([refpoint[i,0],dispvector[i,0]],[refpoint[i,1],dispvector[i,1]], color=[np.clip(1*(1-norm_importance[i]),0,0.8),np.clip(1*(1-norm_importance[i]),0,0.8),1,0.8])\n",
    "    for line in range(2):\n",
    "        for column in range(3):\n",
    "            axs[line, column].set_xlim(min_X-5,max_X+5)\n",
    "            axs[line, column].set_ylim(min_Y-10,max_Y+10)\n",
    "            axs[line, column].set_frame_on(False)\n",
    "            axs[line, column].tick_params(axis='both', which='both', length=0)\n",
    "            axs[line, column].set_xticks([])\n",
    "            if column!=0:\n",
    "                axs[line, column].set_yticks([])\n",
    "            #axs[line, column].set_axis_off()\n",
    "    frames = [f1,f2,f3]\n",
    "    font_size = 16\n",
    "    for column in range(3):\n",
    "        axs[1,column].set_xlabel(str.format('Frame %i' % frames[column]), fontsize=font_size)\n",
    "        \n",
    "    axs[0,0].set_ylabel('Source', fontsize=font_size)\n",
    "    axs[1,0].set_ylabel('Target', fontsize=font_size)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "PlotDispvectorsFrames(sourceAnimation, sourceSurface, targetAnimation, targetSurface, egoCoords[0], 0, 100,200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pose Adaptation\n",
    "\n",
    "To adapt the pose of the target skeleton enforcing the same surface spatial relationship as the souce motion, we denormalize the egocentric coordinates with the parameters regarding the body proportions and bone length of the target skeleton and surface. By inverting Equation \\ref{eq:egocoord_jointpos1}, we compute the correct joint position in the target skeleton of a extremity joint, then we use Inverse Kinematics so that the skeleton reaches the new position.\n",
    "\n",
    "First, we compute the reference point of each mesh component of the target character using the surface estimation from Section Surface Estimation. Then, we use the length of the bone segments to calculate the importance factor $\\tau$ for the target skeleton and multiply it by each normalized displacement vector $\\mathbf{\\hat{v}}_{i}$. Finally, the new position of the joint is given by Equation \\ref{eq:egocoord_jointpos2}.\n",
    "\n",
    "\n",
    "## Inverse Kinematics\n",
    "\n",
    "We use the Jacobian Transpose method for the Inverse Kinematics (IK) to compute the new pose of the avatar <cite data-cite=\"buss\"></cite>. The kinematic paths fed to the IK algorithm are composed of all joints from the beginning of the limb to the limb extremity: the shoulder to the hand and the upper leg to the foot. This allows a separete IK computing for each limb and avoids dragging the spine and the hips in order to reach the target position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Perform Inverse Kinematics for the left and Right hands\n",
    "#you can remove the comments and perform for all limb joints but it will severely increase the execution time\n",
    "JacRHand = ik.SimpleJacobian(targetAnimation, targetAnimation.getskeletonmap().rhand, depth = 5)\n",
    "JacLHand = ik.SimpleJacobian(targetAnimation, targetAnimation.getskeletonmap().lhand, depth = 5)\n",
    "#JacRElbow = ik.SimpleJacobian(targetAnimation, targetAnimation.getskeletonmap().rforearm, depth = 3)\n",
    "#JacLElbow = ik.SimpleJacobian(targetAnimation, targetAnimation.getskeletonmap().lforearm, depth = 3)\n",
    "#JacRFoot = ik.SimpleJacobian(targetAnimation, targetAnimation.getskeletonmap().rfoot, depth = 5)\n",
    "#JacLFoot = ik.SimpleJacobian(targetAnimation, targetAnimation.getskeletonmap().lfoot, depth = 5)\n",
    "#JacRKnee = ik.SimpleJacobian(targetAnimation, targetAnimation.getskeletonmap().rlowleg, depth = 3)\n",
    "#JacLKnee = ik.SimpleJacobian(targetAnimation, targetAnimation.getskeletonmap().llowleg, depth = 3)\n",
    "#start=time.time()\n",
    "#print('Starting IK')\n",
    "for frame in range(targetAnimation.frames):\n",
    "    _ = JacRHand.jacobianTranspose(frame=frame, target=egoCoords[0].getTarget(frame))\n",
    "    _ = JacLHand.jacobianTranspose(frame=frame, target=egoCoords[1].getTarget(frame))\n",
    "    #_ = JacRElbow.jacobianTranspose(frame=frame, target=egoCoords[2].getTarget(frame))\n",
    "    #_ = JacLElbow.jacobianTranspose(frame=frame, target=egoCoords[3].getTarget(frame))\n",
    "    #_ = JacRFoot.jacobianTranspose(frame=frame, target=egoCoords[4].getTarget(frame))\n",
    "    #_ = JacLFoot.jacobianTranspose(frame=frame, target=egoCoords[5].getTarget(frame))\n",
    "    #_ = JacRKnee.jacobianTranspose(frame=frame, target=egoCoords[6].getTarget(frame))\n",
    "    #_ = JacLKnee.jacobianTranspose(frame=frame, target=egoCoords[7].getTarget(frame))\n",
    "    #if np.mod(frame+1,100) == 0:\n",
    "    #    print('%i frames done. %s seconds.' % (int((frame+1)/100)*100,time.time()-start))\n",
    "    #    start=time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def PlotResults(srcAnim, srcSurface, tgtAnim, tgtAnim_Aligned, tgtSurface, egoCoord, frame):\n",
    "    %matplotlib inline\n",
    "    fig, axs = plt.subplots(1, 3, figsize=(16,6))\n",
    "    min_X, max_X = np.inf,np.NINF\n",
    "    min_Y, max_Y = np.inf,np.NINF\n",
    "    for animation, surface, column in zip([srcAnim, tgtAnim_Aligned, tgtAnim],[srcSurface, tgtSurface, tgtSurface],range(3)):\n",
    "        aux = animation.getBones(frame)\n",
    "        bones = []\n",
    "        for i in range(len(aux)):\n",
    "            axs[column].plot([aux[i,0], aux[i,3]], [aux[i,1], aux[i,4]],'-o', color='black')\n",
    "        surf = []\n",
    "        for triangle in surface.headmesh:\n",
    "            vertices = [[vert.getPosition(animation,frame)[0],vert.getPosition(animation,frame)[1]] for vert in triangle]\n",
    "            vertices.append([triangle[0].getPosition(animation,frame)[0],triangle[0].getPosition(animation,frame)[1]])\n",
    "            vertices = np.asarray(vertices)\n",
    "            axs[column].plot(vertices[:,0],vertices[:,1],'-o', color='red', markersize=1, alpha = 0.5)\n",
    "\n",
    "        for triangle in surface.bodymesh:\n",
    "            vertices = [[vert.getPosition(animation,frame)[0],vert.getPosition(animation,frame)[1]] for vert in triangle]\n",
    "            vertices.append([triangle[0].getPosition(animation,frame)[0],triangle[0].getPosition(animation,frame)[1]])\n",
    "            vertices = np.asarray(vertices)\n",
    "            axs[column].plot(vertices[:,0],vertices[:,1],'-o', color='red', markersize=1, alpha = 0.5)\n",
    "        mini_X,maxi_X = np.min([aux[:,0].min(),aux[:,3].min()]), np.max([aux[:,0].max(),aux[:,3].max()])\n",
    "        mini_Y, maxi_Y = np.min([aux[:,1].min(),aux[:,4].min()]), np.max([aux[:,1].max(),aux[:,4].max()])\n",
    "        if mini_X<min_X: \n",
    "            min_X=mini_X\n",
    "        if maxi_X>max_X: \n",
    "            max_X=maxi_X\n",
    "        if mini_Y<min_Y: \n",
    "            min_Y=mini_Y\n",
    "        if maxi_Y>max_Y: \n",
    "            max_Y=maxi_Y\n",
    "                \n",
    "    lenNoLimb = len(srcSurface.headmesh)+len(srcSurface.bodymesh)\n",
    "    #Source Animation ego coords:\n",
    "    dispvector = np.asarray([np.asarray(egoCoord.framecoord[frame].dispvector[i]*egoCoord.framecoord[frame].tau[i])+egoCoord.framecoord[frame].refpoint[i] for i in range(lenNoLimb)])\n",
    "    refpoint = np.asarray(egoCoord.framecoord[frame].refpoint[:lenNoLimb])\n",
    "    norm_importance = egoCoord.framecoord[frame].importance/egoCoord.framecoord[frame].importance.max()\n",
    "    for i in range(lenNoLimb):\n",
    "        axs[0].plot([refpoint[i,0],dispvector[i,0]],[refpoint[i,1],dispvector[i,1]], color=[np.clip(1*(1-norm_importance[i]),0,0.8),np.clip(1*(1-norm_importance[i]),0,0.8),1,0.8])\n",
    "        axs[0].scatter(refpoint[i,0], refpoint[i,1], color='red', alpha=0.5)\n",
    "    #Target Animation ego coords\n",
    "    dispvector = np.asarray([np.asarray(egoCoord.framecoord[frame].tgt_dispvector[i])+egoCoord.framecoord[frame].tgt_refpoint[i] for i in range(len(egoCoord.framecoord[frame].tgt_refpoint))])\n",
    "    refpoint = np.asarray(egoCoord.framecoord[frame].tgt_refpoint[:])\n",
    "    norm_importance = egoCoord.framecoord[frame].importance/egoCoord.framecoord[frame].importance.max()\n",
    "    for column in range(1,3):\n",
    "        for i in range(lenNoLimb):\n",
    "            axs[column].plot([refpoint[i,0],dispvector[i,0]],[refpoint[i,1],dispvector[i,1]], color=[np.clip(1*(1-norm_importance[i]),0,1),np.clip(1*(1-norm_importance[i]),0,1),1,0.8])\n",
    "            axs[column].scatter(refpoint[i,0], refpoint[i,1], color='red', alpha=0.5)\n",
    "            target = egoCoord.getTarget(frame)\n",
    "            axs[column].scatter(target[0], target[1], color='red', marker=\"x\", s=160)\n",
    "    for column in range(3):\n",
    "        axs[column].set_xlim(min_X-5,max_X+5)\n",
    "        min_Y = srcAnim.root.getPosition(0)[1]\n",
    "        axs[column].set_ylim(min_Y-10,max_Y+10)\n",
    "        axs[column].set_frame_on(False)\n",
    "        axs[column].tick_params(axis='both', which='both', length=0)\n",
    "        axs[column].set_xticks([])\n",
    "        axs[column].set_yticks([])\n",
    "        #axs[column].set_axis_off()\n",
    "    \n",
    "    font_size = 16\n",
    "        \n",
    "    axs[0].set_xlabel('Source', fontsize=font_size)\n",
    "    axs[1].set_xlabel('Target', fontsize=font_size)\n",
    "    axs[2].set_xlabel('Target (Adapted Pose)', fontsize=font_size)\n",
    "    \n",
    "    plt.savefig('result.png', dpi=300)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "PlotResults(sourceAnimation, sourceSurface, targetAnimation, targetAnimation_aligned, targetSurface, egoCoords[0], 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "(Em Construção)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Future Works\n",
    "\n",
    "(Em construção)"
   ]
  }
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